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FactorNet: a deep learning framework for predicting cell type specific transcription factor binding from nucleotide-resolution sequential data
Due to the large numbers of transcription factors (TFs) and cell types, querying binding profiles of all valid TF/cell type pairs is not experimentally feasible. To address this issue, we developed a convolutional-recurrent neural network model, called FactorNet, to computationally impute the missin...
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| Publicado no: | Methods |
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| Main Authors: | , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
2019
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6708499/ https://ncbi.nlm.nih.gov/pubmed/30922998 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1016/j.ymeth.2019.03.020 |
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